SOTAVerified

Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

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Showing 51–100 of 1808 papers

TitleStatusHype
EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability DetectionCode1
PG-Attack: A Precision-Guided Adversarial Attack Framework Against Vision Foundation Models for Autonomous DrivingCode1
Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation ModelsCode1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross-DomainCode1
Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular DataCode1
Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based CustomizationCode1
Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation ModelsCode1
Universal Adversarial Perturbations for Vision-Language Pre-trained ModelsCode1
Revisiting Character-level Adversarial Attacks for Language ModelsCode1
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat LandscapeCode1
epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression RecognitionCode1
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point CloudsCode1
RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage GenerationCode1
On the Multi-modal Vulnerability of Diffusion ModelsCode1
Benchmarking Transferable Adversarial AttacksCode1
Fluent dreaming for language modelsCode1
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical ImagesCode1
Revealing Vulnerabilities in Stable Diffusion via Targeted AttacksCode1
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative modelCode1
SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient Vision TransformersCode1
Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity TrainingCode1
Towards Transferable Targeted 3D Adversarial Attack in the Physical WorldCode1
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake DetectionCode1
An Extensive Study on Adversarial Attack against Pre-trained Models of CodeCode1
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language ModelsCode1
Targeted Attack Improves Protection against Unauthorized Diffusion CustomizationCode1
Robustness of AI-Image Detectors: Fundamental Limits and Practical AttacksCode1
Structure Invariant Transformation for better Adversarial TransferabilityCode1
Semantic Adversarial Attacks via Diffusion ModelsCode1
RAIN: Your Language Models Can Align Themselves without FinetuningCode1
Differentiable JPEG: The Devil is in the DetailsCode1
Certifying LLM Safety against Adversarial PromptingCode1
PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model ModificationCode1
On the Adversarial Robustness of Multi-Modal Foundation ModelsCode1
Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed GradientCode1
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial TransferabilityCode1
Multi-attacks: Many images + the same adversarial attack many target labelsCode1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion ModelsCode1
OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated ExamplesCode1
Frequency Domain Adversarial Training for Robust Volumetric Medical SegmentationCode1
Adversarial Attack On Yolov5 For Traffic And Road Sign DetectionCode1
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial AttacksCode1
White-Box Multi-Objective Adversarial Attack on Dialogue GenerationCode1
Fooling the Image Dehazing Models by First Order GradientCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Feature Separation and Recalibration for Adversarial RobustnessCode1
X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item DetectionCode1
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot LearningCode1
Robust Mid-Pass Filtering Graph Convolutional NetworksCode1
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified